It is common to build and deploy supervised machine learning models that are generally comprised of tabular datasets with numerical, categorical, and temporal (date/time) variables. Often though, there may be additional value to be gained by augmenting the model with insights derived from unstructured data (text). Some common examples of unstructured text in this context ...
The phrase “Time is of the essence“ is used to express urgency in all kinds of fields, from legal to medical. In the context of predictive modeling, I like to read it as time being one of the most important concepts to consider. Unfortunately, though, that concept of time is often overlooked. Simply put, what ...
It is often the case with machine learning predictive models that you need to create a model for customer data that spans a variety of “segments” in your business. The business objective (outcome variable) that you wish to apply machine learning may actually be exactly the same across all these segments – even though the segments themselves ...
Data cardinality is an important concept when it comes to Einstein Discovery, but what does it mean exactly? And what considerations should you have around cardinality? Check out this short video to get the answers. Note: Find this video as well as other tips and tricks videos on Einstein Discovery here. Note: The video illustrates ...
Exciting news: with the Spring 22 release, Einstein Discovery supports multiclass classification predictions (Generally Available). This allows you to solve even more predictive use cases for your business with Einstein Discovery. With these Multiclass models, you can predict probable outcomes among up to 10 categories. For example, a manufacturer can predict, based on customer attributes, ...
We have heard of terms such as mobile-first, and now we are embarking on Search First Analytics. 2022 is here, and business people will be pivoting from reading dashboards to quickly searching for insights. Why? Because dashboards can’t keep up with the explosion of data and the natural language search models (even at the enterprise ...
Have you wondered how much data you need to create a good story in Einstein Discovery? Well, you are not the first one with that questions. Check out this short video to understand what it takes to make a good story. Note: Find this video as well as other tips and tricks videos on Einstein ...
Insights, immediate value, and more insights! In Summer ’21 we introduced the all-new reimagined Einstein Discovery for Reports. This product allows you to enhance and augment your experience with Salesforce Reports. With Einstein Discovery for Report [EDR], you can very easily unravel data insights with a couple of clicks within moments. But you might have ...
Let’s assume that your Business Science team has created a great Einstein Discovery model and has deployed it for consumption. As an admin, you are responsible for utilizing this model in your Salesforce instance. In this blog, we are going to go over a specific use case discussing embedding Machine Learning generated predictions inside validation ...
A key pillar in the CRM landscape is pipeline management, where sales representatives track sales opportunities that progress through various stages until closure. This process results in either winning the specific sales opportunity or closing the opportunity without winning the business. The latter may be because the opportunity was lost to a competitor, because the ...
