Most modern businesses know that acquiring a new customer can cost five times more than retaining an existing customer. And it is well established that increasing customer retention by 5% can yield a 25-95% growth in profits. This is why companies are increasingly making retention a critical component of their business strategies.
For independent software vendors (ISVs), customer retention is directly tied to product stickiness. In the "data-is-the-new-oil" era, embedded analytics can play a pivotal role in enhancing product stickiness, as illustrated by the equation below:
Embedded Analytics x Product Usage = Revenue Growth
Yes, you read that right! Embedded analytics can improve product usage by increasing product stickiness, which ultimately results in revenue growth for ISVs.
Product usage pyramid
Moving forward, we'd like to propose a model to systematically deepen product usage by leveraging embedded analytics. For reference, let's call it the "product usage pyramid."
There are three levels to this pyramid:
Contextual analytics: Provide actionable insights within specific contexts of a business workflow.
Democratization: Make analytics accessible to all kinds of users.
Deep analytics: Deepen the accessibility of analytics to make the product stickier.
Let's delve into the details of each level.
Contextualization of analytics requires effort at multiple levels. We will discuss the three core elements that enable this layer below.
1. Seamless integration ensures that both the ISV parent application and embedded analytics application are integrated in a hassle-free, unified way. This can be achieved by enabling SSO/SAML authentication, leveraging a built-in design studio (within the analytics application) to ensure look-and-feel uniformity, and utilizing a robust API library to plug any "gaps" in integration.
2. Contextual embedding positions insightful reports and dashboards within business workflows. Modern embedded analytics platforms offer rich and dynamic dashboards that empower users to consume insights and interact with the underlying data for further, deeper analysis. The onus is on the application providers to ensure these dashboards also have fine-grained access control mechanisms in place.
3. Embedding AI-powered analytical capabilities, like conversational analytics, automated insights, predictive analytics, and generative AI, can help users easily access contextual insights in real time.