Einstein Discovery empowers businesses to explore patterns, trends, and correlations in their historical data with the help of auto-generated machine learning models. While understanding the past is helpful, Einstein Discovery machine learning models can be deployed and operationalized to predict future outcomes – all without ever writing a single line of code. Starting with the ...
Einstein Discovery drives business value for companies by eliminating friction in using machine learning, and maximizing its time-to-value. It is designed to facilitate every step of the journey towards operationalizing Machine Learning in the workspace, in a safe, ethical and most of all practical and easy way. This applies training the model and interpreting the Story, but it ...
The Revenue Operations Analytics template helps you uncover key insights for your Sales Leaders and Sales Reps for a more accurate forecast and better visibility into your pipeline and activity trends. Build up your team with the help of key metrics such as quota attainment, a projected forecast, and account reach to boost your team ...
🇯🇵 Read in Japanese Einstein Discovery allows the business scientist to explore patterns, trends and correlations in business data using Stories. The Story answers various questions, depending on the data it was trained on. Examples include Opportunity win-rate analysis, proprensity-to-buy (PTB) and Case average handling-time (AHT) or satisfaction (CSAT) in customer service. One particularly useful component that results automatically from ...
There is a good chance that you want to create a Machine Learning model for your business to aid decisions, but you are not sure where to begin. Einstein Discovery, located in Tableau CRM (Formerly Einstein Analytics), will enable you to do just that with clicks, not code. Unfortunately, “Machine Learning” and the fancy terms ...
Since the launch of Einstein Analytics over 5 years ago, we’ve been working hard to make the authoring experience richer and more declarative with “clicks, not code”. We hope this has empowered more of you to become dashboard builders and helped you build powerful analytics apps faster for your entire organization. We’ll be taking the ...
🇯🇵 Read in Japanese (Updated 2/10/21) The primary goal of this blog post is to provide technical information on the addition of tree-based machine learning (ML) algorithms to Einstein Discovery. This article helps readers understand the capabilities, benefits, and details of ensemble (tree-based) algorithms in Einstein Discovery. Tree-based algorithms go beyond Einstein Discovery’s traditional focus ...
In order to satisfy increasingly diversified customer demands, product and service portfolios are steadily becoming larger. B2B and B2C companies are expected to guide their customers to the best fitting bundle in their large panoply of products. Predicting the likelihood of a customer or prospect purchasing a particular product is essential and requires a deep ...
The accuracy of Machine Learning models gives rise to one of the most confusing discussions in the world of Machine Learning. There are multiple reasons for that. First, many different performance metrics are used, which makes fair comparisons and transparent discussions hard. Secondly, expectations are often unrealistically high, caused by extremely overblown media coverage of ...
You created a story in Einstein Discovery. You measured its model’s accuracy. Then you made some necessary improvements. The model you deployed is now bringing predictions and recommendations right to your users’ fingertips. You’re pretty satisfied with what you achieved. Cool, you’re done. Your model is out in the wild now. Onto the next adventure, ...
