As the typical SaaS user becomes more data-savvy, providers need to find new and efficient ways to tap into tools that enable them to create actionable insights. To stay relevant in an increasingly competitive market, software companies need to leverage the power of customer-facing dashboards in their platforms to enhance customer service, increase sales, streamline processes, and provide tangible value from existing platforms.
Regardless of the strategic initiative of your platform, the first and main step to develop your customer-facing dashboard is to understand the user requirements and the management process surrounding your data. This in-depth analysis will help implement and maintain a successful SaaS dashboard for your business.
Let’s take a closer look at the three main phases to get started with implementing a dashboard in your SaaS application.
Phase One: Understand the User Requirements
Your product strategy will dictate the goal or intention behind implementing your SaaS dashboard. The more aligned it is to your company’s aspirations, the more value it will convey.
As a data product manager or dashboard developer, you must focus on understanding all of the tools and platforms your users rely on to gather data. This overview, along with understanding what your team(s) wish to gain from the dashboard, will help create a seamless transition and an intuitive approach to fully leverage its benefits long term.
For example, if the dashboard aims to improve customer success, only relevant data should be portrayed and imported from related platforms. This will allow instant access to digestible insights that will help ease the process surrounding a customer’s experience instead of creating distractions with other metrics and processes within different departments.
For data to be usable, it is imperative to break down as many barriers for users to access it as possible. This means understanding their requirements and guiding them towards the easiest way to have critical information at a glance.
Phase Two: Design the Implementation
This phase consists of planning the blueprint for your dashboard and architecting the data model to know how you will then pull the required information from a centralized data lake. To save time and resources on future maintenance, it’s vital to work backward to understand your customer’s business goals—the who, when, and how certain features will be used.
During this analysis, developers should estimate the dashboard’s cost, often represented in three main types: time (or labor), materials, and opportunity cost.
Labor, or time, costs are the easiest to track however, they can still be difficult to predict as everyone in software can relate to 😉 The cost of materials is highly dependent on the consumption of the data management tools, or the usage users are given, which makes this cost a tough estimate. We’ve seen data warehousing costs rise exponentially when projections had the cost increasing at a nice linear pace.
And finally, the opportunity cost compares the features that are delayed in order to build a dashboard with the cost of actually building it. The ROI on a custom-built dashboard will often be less valuable than delayed features for a team.
Phase Three: The Implementation Stage
As part of the implementation phase, many teams forget performance testing, which can ultimately save a lot of time down the road. SaaS users require a highly performant dashboard, so results are returned quickly, and teams optimize their processes. Having a great foundation to begin with will make it easier to continue iterating on your analytics features and avoid stale dashboards in the future.
Building a SaaS dashboard can bring in more revenue and give a clear overview of critical data to make pertinent business decisions. With the proper planning and implementation, you can start seeing the value-added to your business and continue scaling.
Whether you are building internally or using Verb Data to build your solution, you must seek a cost-effective implementation so you can continuously improve your dashboard over time.