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How to Create the Perfect SaaS Analytics Dashboard

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How to Create the Perfect SaaS Analytics Dashboard

Here are a few steps to creating an analytics dashboard that avoids pitfalls and engages developers.

· Big Data Zone ·
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Converting and retaining customers starts with providing a SaaS dashboard that your sales team will utilize consistently. Often, dashboards lack all necessary tools to truly attract employees. Anything from data integrations to user interface and even locating the platform create barriers to maximizing the full potential.

Conducting a path analysis provides insight to understanding user behavior before churn. In most cases, this data will highlight a common action among users that result in abandoning the product.

Identifying this pattern is important when eradicating this problem. Immediately after, segment your users and prioritize those who are at risk of churning.

This path analysis can be used to identify patterns in the most valued users and should be segmented, as well. The goal is quite the opposite, identify what engages them with your platform and apply it to those at risk.

Make Data-Driven Decisions

Ideal SaaS analytics dashboards house essential information that creates a non-biased view of the situation at hand. Using this data to support decisions regarding improvements, added features, and user experience will steer companies in the right direction.

Product managers are often challenged with the unknown impact of newly added features and their effects on KPIs. Pursuing feature A, B, or C is one decision that can be answered with this acquired data.

To eliminate doubt from your long-term roadmap, remove aspects of your product that damage the user experience. With the right dashboard, your sales team can begin to segment this information without knowledge of SQL.

Effective SaaS dashboards help teams focus on what matters. It’s imperative these dashboards have all the necessary data source integrations to inspire action. The perfect SaaS analytics dashboard can have a huge impact on team productivity and achieving goals.

SaaS platforms that maintain the tools development teams find beneficial can largely impact focus and goal achievement. A dashboard with the highest chance of success focuses on presenting personalized actionable insights and is equipped with a brand’s plan to leverage these insights.

Here are a few steps to creating an analytics dashboard that avoids pitfalls and engages developers.

Invest in Data Unification

Unifying all data provides a snapshot valuable to project managers who require the full picture when making strategic decisions to move forward. SaaS companies benefit from extracting customer touch points from various sources (i.e. application and website) and unifying this information to understand customer lifecycle metrics.

Providing visual aids takes this data up a notch and offers another way to understand important insights. Take the screenshot below, for example; this data is specific to dashboard unification capabilities.

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Additionally, cohort analysis by acquisition channel is depicted in the image below and promotes an understanding of which channels currently maintain their standing with the highest or lowest retention rates.

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Recognizing when one specific user uses multiple devices is vital to understanding the customer lifecycle. This is often missed when dashboards count each device used as one unique user.

Consider someone booking a vacation through Expedia.com using their cell phone to search for hotel accommodations and their desktop to find flights. Traditional analytics identify this as two separate sessions. Now, with the correct software, brands like Expedia can group this fragmented data into one user experience. The result is a comprehensive understanding of how people use multiple devices that leads to an improved user experience.

Conduct Behavioral Analysis

Analyzing behavior brings brands closer to discovering key correlations between user actions and desired outcomes. Similarly, companies can take note of what causes churn.

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saas ,big data ,data analytics ,data visualization ,behavioral analytics

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