Product managers have a lot of decisions to make. It’s a position that regularly touches plenty of other internal functions, but ultimately is responsible for pulling together all of the pieces from multiple sources and building a product strategy. Those choices have long-lasting impacts that extend beyond a product, into the bigger picture of a business and its customers. In order to make the best decisions, it’s imperative that product managers have access to the right data at the right time, organized in a way that focuses on the most important pieces.
When it comes to my dashboards, product managers often rely mostly on quantitative data to provide summaries of large datasets, but there are also plenty of other ways to use qualitative information. When making product decisions at Sisense for Cloud Data Teams, data can help with many different facets of the process. There are three general types of Sisense for Cloud Data Teams dashboards used to make decisions as a product manager, here’s a quick rundown of each:
Ongoing KPI monitoring
The goals of a product manager are the same as the goals of the entire company: retain current customers, attract new ones, increase product usage, etc. It’s vital to a product manager to keep an eye on the top-level business metrics. At Sisense for Cloud Data Teams, this data comes from Salesforce (sales data), our production database (user activity data), and our team of analysts (custom-created enhanced metrics). This type of monitoring always looks at aggregate numbers rather than any specific drilldown or segmentation; the goal is to view whole product and the whole user base to keep a pulse on the way they move together.
Every day, the Sisense for Cloud Data Teams platform delivers these dashboards via email, which usually has all of the information I need. It also includes the option to click into the dashboards to dig deeper when something looks out of place. When I scan those dashboards, I’m looking for any numbers that stand out as unexpected.
Specific feature tracking
A lot of the hard work of product management goes into the building, launching, and optimizing features that address/solve specific use cases of our customers. To do that well, I need to create dashboards that track features starting at high-level adoption and drilling down to the actions of individual companies and users.
We put a lot of importance on using the data team to ensure that data is being treated properly and producing the best insights. When we launched Data Discovery for Business, I worked with our data team to build a dashboard to tell me everything about the adoption of that new feature: which customers were using it, how many individual users, how many charts were they creating, what types of charts they were creating, what job functions were using the new feature the most. To create these dashboards, I met with the data team to discuss what questions needed to be answered. I could have typed SQL to create charts on my own, however, to avoid the pitfalls of data, I needed help from our data scientists.
Like the first use case, I get daily emails that present these reports, but in this case, those emails are just the start of my exploration. I usually see those reports come in and instantly click into the Sisense for Cloud Data Teams platform to start getting more information. It’s vital to product management that we keep thinking of new questions and monitoring movement throughout the product lifecycle to ensure success.
Specific questions and one-off requests
Like any other function at a company, product management work is very rarely contained to a limited range of tasks; I collaborate with a lot of different teams and I need a way to manage one-off requests efficiently. Sometimes that means sorting the customer base in a certain way to identify a specific customer or set of customers for a beta test. I might need data to answer a specific question about a feature that wouldn’t otherwise be a part of the regular monitoring. The range of questions that need to be answered is unlimited, so I need a way to prioritize the questions, build answers and track status.
For example, Sisense for Cloud Data Teams released a backend update that hit our customer base in phases. To determine those phases, I needed to combine information about their size, tier, health score, payment plans and more. There was no way I could have proactively prepared for this segmentation, but when the request is made, I need to be able to work with my data team to create a dashboard like this quickly and accurately.