Einstein for multiple objects - Salesforce Tutorial

- [Instructor] So, kind of how I broken up this section is that we're going to start off with the multiple objects. So we are going to talk about things and tools that could be used across the objects, and then once we get into each specific object, then we'll talk about different examples for these, ones that can be used across multiple. So hopefully that makes sense. Let's go and jump into Einstein for multiple objects. So the first is going to be Einstein Discovery. This is going to be a BI tool, or a business intelligence tool, and it's going to help you draw conclusions from your data that you might not otherwise see. So essentially what it does is that it looks at patterns from your data over a specific time period and helps you understand. One thing that this might be super useful for is understanding your best lead source that converts the most. So let's say maybe social outreach really helped, or cold calling was really great for bringing in more revenue. That would be a really great thing that Einstein could help you determine, as well as maybe what countries, or just help establish a pattern. This one works mostly in Tableau, which is also a Salesforce product, if those two are connected, to help you just deal better with your data. And again, this can be used across multiple objects, even I think across multiple report types, which are going to just be, if two objects are put together, what you're reporting on across those two objects. So that is Einstein Discovery. It's a pneumonic for this, helping you discover insights from your data. So discovery all about data. All right, next is going to be Einstein Prediction. So mostly Einstein Prediction Builder. So this can be used across multiple objects, typically used at one object at a time. So essentially what this does, takes data from your object to use the existing fields that you have to predict a new field that you have. So an example of this could be like a likelihood simulator of like a likelihood for a lead to convert based upon certain fields, like how warm the lead is, how many contacts you've had with the lead, or how old the lead is. It could take those into consideration and then predict if it is going to convert or the likelihood of it going to convert. And then we've got Einstein Next Best Action. And this is going to be one that is going to be on one object at a time. It's similar to the Prediction Builder where you'll need to build the model beforehand and you'll need to train the model, and then it will spit out whatever the output is. So for the Prediction Builder, it's a prediction. For Next Best Action, it'll be the next best action. So let's say the next best action is sending out a followup email, it will let them know. This uses a flow-like builder. So if you have been using flows, this will be super familiar to you. It just leverages AI. And then we have Einstein Language. So it uses language processing or natural language processing to help customers with written communication. So it can help detect sentiment, so what the customer means, how they're feeling, as well as can detect intent, so meaning what they really want to get out of the conversation. This can be super useful if you are using bots. Next is going to be call summaries. So it's going to take your calls that you have, I think currently it is only available in English, and it helps output a summary based upon what was said in the call. It helps a lot with data keeping and making sure that you are staying on top of the clients. It helps retain the clients. So as you can see, it output next steps, which can be super useful for keeping your salespeople or your service people accountable and on track. Not to mention that this can save an incredible amount of time, that it does this automatically rather than the person on the phone call having to summarize it themselves.