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.
Contextual analytics
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.
Democratization
Making analytics accessible to all is a vital step towards increased product usage. Modern analytics platforms offer an array of self-service capabilities. We'd like to highlight some powerful analytics capabilities ISVs should look out for in pursuit of democratization.
AI-powered inline data preparation: In the past, IT teams were responsible for data cleansing and preparation. Today, BI & Analytics platforms offer AI-powered self-service data preparation capabilities to quickly cleanse, transform, and enrich data.
Drag-and-drop visualization builder: Most business users can perform ad hoc analyses by creating their own reports and dashboards. They expect to find a drag-and-drop visualization builder in any analytics application they use. ISVs should ensure their embedded analytics provider offers extensive features and options with respect to building data visualizations (like a robust chart library).
Conversational analytics: Users should be able to get the insight they're looking for by asking simple questions to an AI-powered conversational assistant.
Automated insights: Users can gain a deeper understanding of their data visualizations, and discover essential insights, through AI-generated auto-narratives.
Deep analytics
The depth of analytics capabilities a platform offers is directly related to a user's ability to improve the overall product stickiness necessary for high user retention. We'd again like to highlight a few capabilities ISVs should look for.
Robust visualization options: Today's businesses work with a wide variety of data types, like discrete and continuous data, geo-spatial data, and qualitative data. ISVs should ensure their embedded analytics provider offers a range of visualization options to cover the entire spectrum of essential data types.
Predictive analytics: Successful businesses have historical data readily available. But to make decisions successfully, you need foresight. Modern analytics platforms offer predictive analytics capabilities to serve this purpose. The key factors that ISVs should check for include a) the breadth of forecasting models available and b) the depth of pre-built ML models supported.