In this special guest feature, Dave Hurt, CEO and Co-founder at Verbdata, discusses how building, implementing, and maintaining dashboards requires a deep understanding of how to invest resources and time in product and engineering. Dave is on a mission to build a lasting company through a great team and strong customer relationships. Previously, he was an early employee at ONOSYS and co-founded Prototype1, a prototyping and development agency acquired by a leading customer.
A business thrives on making valuable decisions based on reliable and updated metrics. According to a recent study conducted by MicroStrategy, about 57% of companies worldwide use data to drive strategy and change within their business. This comes as no surprise in today’s SaaS world in which dashboards are one of the most requested features across the market.
The challenge lies in investing time and resources wisely when building and maintaining an effective customer-facing dashboard, creating a powerful analytical tool to give you that competitive edge.
Focus on the Foundation
When creating dashboards for your customers it’s imperative to cut through the noise to understand what they truly need to improve their performance and what will help you sell your products. Beginning at the end will help you build a better foundation for your customer-facing dashboards because you’ll understand what data is needed, how it’s needed, and when it’s needed—all of which inform the infrastructure decisions that must be made to create a great customer data experience in your application.
A centralized source that brings the necessary data together and prepares it for use in a dashboard is the bedrock that any good dashboard is built on. Not every dashboard requires data from multiple sources. Centralizing data in a data lake may not be required for your instance but preparing the data in a data warehouse provides the necessary scalability that is required for a growing platform. It’s common to cut corners when first building a dashboard by using a replica or even build from a production database but neither of these scales well and will ultimately impact the rest of the system as your data and customer set grow.
Usage Data on Their Data
Understanding what customers want helps build dashboards, understanding how customers use them helps improve customer dashboards. To better understand the performance and value that customer dashboards provide, set up product analytics on them. Track things like frequency of use, duration of use, and areas or features that users focus on when using the dashboard.
It may seem like an obvious step to take but most product teams that we’ve met with, build their customer-facing analytics and rarely think about it again. Deciphering the data requires some additional thought. For example, when users spend a long time on the dashboard section it can mean different things for different platforms. For some, an extended session could mean that the data is confusing and difficult to comprehend, while for others it means that users can pull many insights from the dashboard. Think about what the right usage metrics are for your platform and users.
Customer Support has the Data
Customer-facing dashboards are often a game-changer for customer support and success teams. When customers having direct access to their data customer support teams can focus on more value-add activities instead of running reports on behalf of their customers. Once a dashboard has been implemented the data about customer inquiries should be used to understand the quality of the new data experiences.
Product and engineering teams should look to the customer support inquiries to see if there is a change in requests around customer data after dashboards are implemented. Based on this data, teams will see if their dashboards simplified or complicated their customer support team’s life. If requests have increased it’s worth a dive into understanding what and why.
Speed is Quality
No one likes to wait longer than they want to, even for a few seconds. The time it takes a dashboard to load has a huge impact on the perceived quality of the platform. Load times over 10 seconds are the upper limit that we should allow in customer-facing platforms. Unfortunately, shaving a few seconds from the time it takes a dashboard to load can require a lot of work and resources.
Data experiences have an interesting effect in that as they become more successful and used more they become more difficult to maintain. A dashboard that loaded in a second last month might take a minute next month due to many factors—increased data size, schema changes, etc. To maintain an optimal dashboard for your customers, you must continually monitor response times and allocate time to the maintenance of the features.
Allocating Time
When implementing a new feature people often forget to account for the ongoing maintenance that it will need. This is especially true and important with customer-facing dashboards because they slow down product development even when new features aren’t directly related to the dashboard. For example, a new feature can create new data that needs to be visualized on the dashboard or the feature may require a schema change that affects that data model used to support the dashboards.
When implementing new features, regardless of the dashboards’ involvement, always allocate time to manage the impacts it may have on the dashboard.
Building, implementing, and maintaining dashboards requires a deep understanding of how to invest resources and time in product and engineering. While this task can be laborious, to begin with, planning ahead not only ensures customer satisfaction and optimum performance but provides actionable insights to assist the decision-making process.
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Ultimately, creating a customer dashboard comes down to keeping the user in mind. Keep the experienced streamlined, ensure that it’s fast and responsive, and that their needs are always kept in mind.