Tesser Insights - Fine Tuned User Story?

Tesser Insights, a unified self-serve analytics SaaS platform for experimentation, solving, and sharing for business users.

Challenge: Traditional methods of data analysis were often slow, expensive, and required specialized technical expertise. Tesser Insights wanted to create a self-serve analytics platform that could be used by business users without requiring specialized technical knowledge or significant investments in hardware or software.

Solution:

Building a Self-Serve Analytics Platform: Tesser Insights developed a self-serve analytics platform that allows business users to perform data analysis, experimentation, and modeling without the need for specialized technical expertise. The platform is cloud-based, which means it can be accessed from anywhere, and it has a user-friendly interface that makes it easy to use.

Gathering and Analyzing Data: The platform allows users to gather data from various sources, including databases, APIs, and spreadsheets. Users can then analyze the data using a variety of techniques, including clustering, dimensionality reduction, and anomaly detection.

Developing Predictive Models: Based on the insights gained from the data analysis, users can develop predictive models to forecast outcomes and make informed decisions. The platform includes time-series analysis using ARIMA models, and machine learning models such as random forest, decision trees, and neural networks.

Sharing and Collaborating: The platform allows users to share their analyses, experiments, and models with others in the organization, allowing for greater collaboration and knowledge-sharing. The platform also includes a library of pre-built models and experiments that users can use as a starting point for their own analyses.

Providing Actionable Insights: The platform provides users with actionable insights that can be used to inform their decisions. These insights include specific recommendations for production scheduling, inventory optimization, promotion effectiveness, product assortment optimization, and sales forecasting.

Continuously Monitoring and Updating Models: The platform continuously monitors the performance of the predictive models and updates them as needed based on new data and changing business needs. This ensures that the insights provided remain accurate and relevant over time.

Results: By providing a self-serve analytics platform that can be used by business users without requiring specialized technical expertise, Tesser Insights has helped organizations extract insights from their data quickly and efficiently. The platform has helped businesses optimize production and inventory levels, reduce stockouts, increase sales and profitability, and improve collaboration and knowledge-sharing within the organization.

Conclusion: Through the use of a self-serve analytics platform, Tesser Insights has made it possible for businesses to extract insights from their data quickly and efficiently. By following the steps outlined in this example, Tesser Insights has helped organizations make better-informed decisions, reduce costs, improve efficiency, and increase profitability. The platform has also helped to democratize data analysis, making it accessible to business users across the organization.