Einstein Analytics — Your Friend to Manage Data

Introduction

Artificial Intelligence has seeped into human lives thus making it easier. It helps to improve how we live, work and entertain ourselves. From voice-powered personal assistants, to more underlying technologies like suggestive searches, predictive capabilities and behavioral understanding, there are several applications where AI has smoothly fit in.

AI enabled us to showcase data visualization and to perceive complex data patterns. This helps businesses to acquire higher choices, improve insights and make data interactive with others. Through this technology the incremental rate of creating errors is also minimized.

Salesforce being world’s №1 CRM, conjointly deals with the important information associated with the organization’s day to day business. Einstein Analytics also previously called as Wave Analytics permits you to drill down within all your data quickly just by providing AI-powered advanced analytics, right in Salesforce.

Here is the initial series of blog posts and I would really like to elucidate on one use case to showcase how Salesforce Einstein will facilitate to unravel real-time problems with minimal or no code.

Use Case

Salesforce Sales Cloud is a platform designed to manage the sales, marketing and customer support in both B2B (Business to Business) and B2C (Business to Customer) context. By using the Sales Cloud, the key business goals that can be achieved by it is to close deals faster and more effectively.

Let’s assume the organization is losing its potential clients. Most of its opportunity are closed in the lost stage. To enhance the sales of the organization, knowledge must be analyzed to grasp the underlying reason of the opportunity lost.

Implementation

Visualizing the information is more convenient than delving into the complex data table collections because the human brain easily interprets the graphics, unlike the cumbersome Excel spreadsheets due to their lack of information overload. Einstein could be a glorious selection for the users to get the Pipeline Movement Report which will assist you to research the pattern of the lost opportunities.

1. Get the specified set of information

The first step is to extract and integrate the data that may be used for data visualization using Einstein for an improved understanding of a customer’s business.

A dataflow is a set of instructions that defines what data to extract from Salesforce Object’s or from the existing dataset that was created from some external data source. All these instructions are contained in a JSON structure.

Using dataflow, we can transform and merge the existing data to create the required dataset. There are various types of data transformations such as SFDC Digest, Edgemart, Augment, ComputeExpression, ComputeRelative, Flatten and SFDC Register.

The most important data transformations used to analyze the pipeline movement is ComputeRelative. We can get the following data using the ComputeRelative transformation:

· Age of the Opportunity pipeline.

· First/Last record value from multiple child records of a single parent.

· Revenue change in the opportunity.

2. Generate Dashboards

Sales pipeline report are useful to assist one with all the sales pipeline management requirements. With a quality sales report, one can start to chase contacts, estimate what percentage of deals can be closed and predict sales, quicker than ever.

Let’s go through some of the potential reports created in Einstein that can help to boost the sales:

Key Business Metrics:

Every organization have some key business metrics through which they measure the performance of the organization. A dashboard should give you quick insights on the key business metrics. The best way to represent that is the number widget on the dashboard. Also, with Einstein we can change apply the filter widgets and the values will be changed dynamically. This can be achieved using the bindings between two or more widgets in the JSON structure of the dashboard.

Analyze What Went Wrong

The Challenge is to Identify the deals those are at risk and can be lost in the near future. The Sales rep should have such deals on his dashboard so that they and take appropriate action to retain the deal.

All the data related to age of opportunity was calculated earlier in the data flow with the transformation ComputeRelative. This can surely improve the conversion rate of the Opportunity.

Opportunity Pipeline Report — Long Term

This shows all open Opportunity whenever the month of the year starts. These opportunities can get closed later in the month, but this doesn’t matter.

Here we can even have another table widget, and which can show the records and their details.

For example: If we select orange bar in the month of October, the table widget will show all the records with Stage = Qualification those were open at the beginning of the month October.

Create Stories — Einstein Discovery

Einstein Discovery stories help you to know what are recommended course of actions that you can take to improve the Sales Pipeline. It enables users to recognize numerous patterns by accessing your data without any extra effort. It’s like to have your own personal data scientist on work. It can answer the following questions:

· What Happened?

· Why it happened?

· What might happen in future?

This helps to understand the undiscovered insights even to a person who is new to the system and facilitate them to take effective course of actions.

What more can be included

Einstein is not limited to data visualization we can even predict what is going to happen to the opportunity. We can include following tools of Einstein to enhance the above model powered with AI.

· Prediction Builder

· Einstein Search

· Next Best Action

We will be talking on these in the next part of this series. Feel free to drop your queries and feedback.