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 ...
If you have been following along in this blog series, I’m sure you are starting to see how powerful SAQL can be joining data as we see fit, but there is more to the story. You can create new derived fields that we already touched upon in part 5 of this blog series, but let’s ...
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, ...
Working with Einstein Analytics I often hear people finding the data manager the most challenging part to learn. Perhaps because when people work with dashboards in core Salesforce we don’t have to shape our data the same way, we simply need to understand what objects we want to use in our report or dashboard. There ...
A little more than a year ago I sat out to demystify bindings (or interactions as they are being called from Spring 20). I wrote a blog series covering data selection, data serialization, and data manipulation functions. It turned out to be quite a few blogs and quite a few bindings as well. I figured ...
Einstein Discovery is AI-powered analytics that enables business users to automatically discover relevant patterns based on their data – without having to build sophisticated data models. However, Einstein Discovery is only as good as the historical data it is analyzing. Wrangling the data, which is always the hardest part, involves cleaning and consolidating your historical ...
I’ve heard more than once than bindings can be hard and confusing. And if I didn’t believe that, then I only have to look at the most viewed blogs I’ve written and my demystifying binding blog series is by far the winner. In the Spring 20 release, it has become a little bit easier to ...
Before getting started with creating first your Einstein Analytics Plus Stories, there are some things you need to think through and decide. Typically people are concerned about data, but we will save that for later. But even before rolling up your sleeves and finding and crunching data, the most important pre-creation task is the need ...
When you are new to Einstein Analytics and you for the first time have to build a dataset it may go alright, yes there are a few new concepts to understand especially the grain level (or root object) but you can manage. When it comes to making additional modifications in the dataflow afterward that’s where ...