We’ve been speaking to Alejandro Pérez, CEO of Komet Sales, a business-to-business SaaS platform. The platform enables flower businesses to manage everything necessary to run a flower operation at an import and wholesale level. Here’s what Alejandro had to say about how Komet Sales has defined and communicated KPIs.
How did you define the metrics for your team to focus on?
It was a learning curve. At first I picked a bunch of ego driven vanity metrics and chucked them up on a dashboard, such as how many flower deliveries we had facilitated in a day. However, this wasn’t improving my team's ability to understand the impact of their work on the success of the business or, more importantly, improving my customers’ lives.
From there I started questioning how each metric we were looking at could help improve our customers’ lives and any data that wasn’t actionable was removed from the mix.
Then I took it a step further and got the team involved in the design process. I’d create a starting point for discussion by displaying a set of KPIs, put them in front of the relevant team for a period of time and encouraged them to question if those metrics were interesting to their day-to-day work. Following that, I’d iterate based on the feedback.
I wanted to awaken their curiosity by engaging them in conversations around the KPIs, and empower them with the decision to define what mattered. This level of ownership engaged them with the metrics, but also ensured everybody really understood what the numbers and abbreviations in front of them meant.
To this day, I go over the metrics and visualizations every 2-3 weeks. They are constantly evolving since what matters to us changes over time. I also think these regular tweaks prevent the screens from becoming part of the furniture, becoming invisible.
How do you communicate metrics to encourage the team to view and act on the data?
We put them up on dashboards on TVs in our office. I started with one TV but soon realized that wasn’t enough due to how different the data needs of each person were, so I went a little crazy and got 14 screens. They’re now strategically placed in our open plan office so that the relevant dashboards are visible to the relevant people and it allows everyone to see the data at a comfortable glance from their seat. My dev team even have two mirroring screens displaying the same KPIs so nobody has to turn around to see their data dashboard.
The dashboard design is done both portrait and landscape to best accommodate the data they display. The information on the various dashboards is split to cater for the different teams.
We’ve got a couple of technical dashboards displaying things like throughput, uptakes, number of connections, uptime and error rates. There’s also one customer service dashboard, one sales dashboard and several project specific dashboards. The sales team is in some ways involved in customer service too, so they see both dashboards on a loop on the same screen.
Customer Service Dashboard
Also, I need to have a bird’s eye view of the whole operation, so the screen next to my desk runs on a loop, displaying all the various team dashboards in succession.
Building a data informed culture is tough, how did you measure success?
In my opinion, you can never expect every single person in the company to maximize everything available to them.
For me, success with the dashboards and data came when I could see that a core group of employees regularly digested the displayed data and took actions that positively impacted the business.
How has defining and communicating metrics in this way helped your business?
As a software as a service (SaaS) business, we produce a lot of data and it’s very time-consuming to filter through that regularly in order to get something actionable out. Defining metrics and communicating them in this way has helped solve that problem for us and make the data instantly accessible to the people who need it.
Having these real-time metrics on display encourages and motivates my staff to do a better job. It also democratizes the monitoring of key metrics, which means everyone here feels responsible for the numbers and for improving them. For example, we’ve seen average response time for customer tickets drop drastically since using this approach to data.