We previously compared Sisense and Tableau but we wanted to compare Sisense to another similar, and popular, tool Power BI. Both tools have their specific uses and we will go over them today so that you can choose the best product for your project.
Indellient worked with SkuVault from KPI conception, metric and calculation definitions, dashboard design, data ingestion, through to implementation of Sisense into their existing platform.
Data Warehouses have so many options: On premises or Cloud? And then from there what tools should you use to access the data? We discuss all your options in this easy to read blog.
Today we discuss how to reduce Bad Data by identifying where it comes from and how to stop it from the root. We also discuss improving formulas and other analytics insight through best practices.
Improve Customer Lifetime Value is as simple as making a great customer experience while shopping and giving them an incentive to staying loyal. We discuss how to do that exactly in this blog.
Applying AI and Machine Learning algorithms to e-Commerce is transforming customer experience. Intuitive upselling, fraud detection, and more we discuss the current trends.
Stop tracking your OmniChannel Retail Analytics in Excel. We discuss the benefits and shortcomings of SaaS Analytics Platforms vs Custom Data solutions.
We created easy to save infographics that help you with the important marketing analytics formulas like CLV, CCR, CAC, and NPS.
What are Data Warehouses and what do they mean for eCommerce? Today we give a brief intro to Data Warehouses and the benefits they bring to eCommerce businesses.
When choosing between IaaS and SaaS there are a lot of benefits to consider: Scalability, Performance, Cybersecurity, and more as we go into details on why you might choose IaaS for your business.
Indellient developed and maintains a data-mart with powerful reporting capabilities using AWS data services. The stakeholders now use this platform daily to track daily progress.
The dynamic CLV calculation takes each client’s background information, product usage, historical complaint, and billing data as input, and generates a unique predicted customer lifetime value as output