What is CRMA and what is it used for?

By the end of this section, you should have a good grasp of what we are referring to when we speak of CRMA, and how it is used in the real world.

The questions what is CRMA? and what is it is used for? will be answered in the following sub-sections.

What is CRMA?

CRMA is not a Salesforce app – CRMA is a data analysis and business insights platform with integrated machine learning insights from Einstein Discovery.

To expand upon the high-level summary shown in Figure 7.1, CRMA is a platform that combines the following features:

• Native, two-way integration with the Salesforce CRM platform.
• On-platform data extraction, combination, and transformation in the data manager.
• External connectivity to a variety of other platforms and cloud storage providers.
• Data visualization, analysis, and exploration.
• A data action framework to enable decision-making based on insights.
• Embedded intelligence from the Salesforce Einstein platform, such as Einstein Sentiment analysis.
• Insights can be shared in various ways, such as Salesforce Chatter, URL generation, and downloads.
• On-dashboard notifications to alert users when certain criteria are met.
• A mobile TRCM Analytics App.
• CRMA/Einstein Analytics APIs and SDKs.
• Various data security tools to mask incoming data and restrict/filter outgoing insights as required.

At a technical level, as far as architecture goes, here are some details:

• In CRMA, data is searched for using an inverted index that enables fast query results – datasets containing up to a billion rows can be queried in seconds.
• CRMA data is loaded into a non-relational store via a dynamic, horizontally scalable key-value pair approach.
• The CRMA workflow engine is a software application that is designed to help users enforce a series of recurring tasks that make up a "business process" or "workflow." It applies limited transformations upon data ingestion but largely stores information in its original form.
• Data is consumed and stored in a non-relational inverted index as key-value pairs. These key-value pairs only store non-null data values, which improves efficiency and speed.
• CRMA's query engine employs techniques such as vector encoding, differential encoding, and incremental encoding to compress data and perform efficient and faster queries on compressed data.
• Quantitative data is queried in a columnar store in RAM across Salesforce's cloud.
• CRMA provides Einstein Analytics Actions, which allow you to seamlessly go from question to answer to action without logging into a separate solution – such as creating a task, updating a record, creating a case, posting to Chatter, and more!
• Heavy compression, optimization algorithms, and parallel processing fast and efficient queries on very large datasets.
• Mobile-first design – CRMA enables data creation, analysis, and action directly from mobile devices.
• Einstein Analytics is an open, scalable, and extensible platform. Being built on the secure infrastructure of Salesforce, the world's top CRM, CRMA provides easy-to-use APIs that enable deep relationships with other tools and platforms.
• Salesforce has worked with Informatica, MuleSoft, Snowflake, and other vendors to build interfaces with BI and data solutions out of the box.
• CRMA delivers fast-rendering graphics that connect the relationships between views of data by drawing SVG graphics within the browser and using an animation engine faceting and filtering. This demands fewer resources from the user's device or the server.
• The CRMA platform is built upon Salesforce's trusted, multilayered approach to data availability, privacy, and security.

Here is a visual representation of Salesforce and the CRMA architecture:

Figure 1.2 – Salesforce and the CRMA architecture

The following is a simple graphic that explains how CRMA and Einstein Discovery (referred to as Einstein Analytics Plus) fit within a typical data architecture that includes Salesforce:

Figure 1.3 – The CRMA and Salesforce technology stack

As you can see, the business outcome of CRMA is intelligent experiences built upon the stable, secure platform of the world's #1 CRM, Salesforce.

What is CRMA used for?

CRMA is used to extract, aggregate, transform, and visualize data. That is, it is used to take raw data, process it with the aid of business acumen, and tell a story.

For example, I created the following CRMA dashboard for a business in the property development space with dummy data:

Figure 1.4 – A CRMA dashboard that visualizes property development information

What is the purpose of this dashboard? How did I use TRCM in this use case?

1. I loaded the raw data tables into CRMA.
2. I combined these tables into one dataset using CRMA.
3. I transformed the data to prepare it for visualization in CRMA.
4. I used TRCM to design, build, and share the analytics dashboard.
5. I was able to "slice and dice" the data, diving into the information to make sense of it and tell a story, with CRMA.

Now, CRMA is just one of several data analytics tools in the Salesforce ecosystem. Some others are as follows:

• Salesforce Reports and Dashboards
• Einstein Analytics
• Tableau
• Datorama
• Pardot analytics
• Marketing analytics

The main three that we are concerned about in the context of this book are Salesforce Reports and Dashboards, CRMA, and Tableau. How do these analytics work together? Here is a simple overview:

Figure 1.5 – How do the three main Salesforce analytics tools work together?

Which analytics tool should you use for which business use case?

Here are some questions to consider (see https://marktossell.com/2020/06/20/tableau-or-einstein-analytics-which-is-best-for-you/ and https://marktossell.com/2020/10/31/are-you-confused-about-einstein-tableau-and-tableaucrm-read-this/ for more detailed information):

• Who is going to use the business insights?
• Where are they going to view and action the insights?
• How will they share those insights with others?
• How much of the data for the dashboards is stored in Salesforce?
• Where is the external data stored? 
• How will external data be connected to the BI tool? 
• What in-house resources will work on the analytics tool?  
• What platforms are users accustomed to using? 
• How many of the data consumers are Salesforce CRM users?
• What BI platform license structure suits the business best?

The following diagram shows a very simple way of looking at the comparison between CRMA and Tableau:

Figure 1.6 – A simple comparison of Tableau and CRMA

Now, you might be wondering, how are CRMA and Tableau different? The following tables will help you make sense of the differences between the two platforms:

Figure 1.7 – Differences and synergies in the architecture for Tableau and CRMA/Einstein Analytics

When it comes to Salesforce Reports and Dashboards, how do you know when you need to upgrade to CRMA? Here are some points to guide you:

• You need to incorporate external data.
• You want contextual record actions to action insights in CRM.
• You want to use embedded analytics.
• You require greater customizability.
• You wish to use the Analytics mobile app.
• You need predictive insights and augmented analytics.
• You want to take advantage of CRMA pages.
• You need advanced trending and waterfall charts.
• You require flexible and powerful data modeling in the data flow editor and data recipes.
• You want superior performance for up to 10 billion rows.

Real-life examples of how to use CRMA

The best way to communicate how CRMA can be used in a business is by way of four real-world case studies. The four case studies that we will go through are as follows:

• Out-of-the-box sales analytics
• Student success insights for higher education
• Property development insights
• Marketing attribution analytics

Out-of-the-box sales analytics

A very common and highly effective use case for CRMA is the suite of sales analytics standards that are available as a standard template. With some simple configuration, you can have powerful insights into your Sales Cloud data within minutes. These insights include the following dashboards:

• Sales Analytics Home: Provides an overview of high-level key performance indicators (KPIs).
• Leaderboard: Gives leaders a summary of the team's and individuals' performance, including quota attainment, pipe coverage, bookings, pipe generation, closed-won business, average sales cycle time, and sales activities.
• Trending: Analyze pipeline changes over time, including the beginning and end values of the pipe, as well as what's moved in and out.
• Sales Stage Analysis: Visualizes how deals have moved through stages of the sales process, revealing bottlenecks and at-risk opportunities.
• Whitespace Analysis: Identifies resell and upsell opportunities.
• Executive Overview: Sales executives can review the pipeline's status, the projected closing, and top deals by lead source, plus high-level views of sales and service performance.

You will learn more about this app in Chapter 3, Connecting Your Data Sources.

Student success insights for higher education

It was exciting for my team and me to collaborate with over 20 higher education institutions in the United States, including Harvard and Cornell universities, to employ the power of Einstein and CRMA to address some of their most pressing challenges. While there are many exciting opportunities to consider in the higher education space, we initially focused on the area of student success and persistence:

• If schools could identify warning signs for students before they failed to meet the criteria for persistence, the school could act proactively to assist the student.
• If schools could predictively identify students at high risk of not persisting, based on certain key characteristics of those students, then those students could be afforded an extra level of support right from the start.
• If smart systems could suggest what actions might help a student to persist, the school would be much more effective and efficient in enabling student success.

No one has been able to solve this problem, though many have tried. However, with the power of Einstein and CRMA, we have developed a successful MVP prototype for student success, and we are engaging several colleges to build this intelligent solution for them. Here are some screenshots of the Student Success Persistence tab:

Figure 1.8 – Sample dashboard view of the student success solution in CRMA

The following is the Student Success At Risk tab:

Figure 1.9 – Sample dashboard view from the student success solution in CRMA

The following is the Student Success Action List tab:

Figure 1.10 – Sample dashboard view from the student success solution in CRMA

For a video of this solution, see https://vimeo.com/329929308.

Property development insights

In this use case, the client was a Sydney apartment developer. They had a growing, thriving business, they were using Salesforce extensively, and they had bucket-loads of data, but they were unable to do the following:

• Quickly drill down into sales data using various variables.
• Perform actions from the analytics dashboard.
• Prioritize clients by the available equity and loan to value ratio (LVR).
• See the total area and $ value of purchases versus target.
• See the trending total area and $ value of purchases.
• Project growth in 5 to 10 years for amounts reinvested.
• Present analytics to a client on a tablet that showed the current and projected state.

The CRMA platform, combined with their vision and our expertise, enabled us to build a solution that provided the client with insights that they had never seen before. They were finally able to go from data to insight to action.

Here are some screenshots from our property insights solution.

Here is the home page for the Property Development insights dashboard:

Figure 1.11 – Sample dashboard view of the Property Development solution in CRMA

This is what the view looks like for the user on the field:

Figure 1.12 – Sample dashboard view of the Property Development solution in CRMA

Marketing attribution analytics

We were asked by a marketing business in the health care space to build advanced analytics around marketing attribution, with the end goal of delivering a closed-loop attribution analytics solution. This was quite the challenge!

1. Our client was unable to show actual and projected return on marketing investment for their clients. The strategy was very hit-and-miss.
2. The client's customers had large amounts of data but were unable to utilize this data to make informed decisions around marketing channels.
3. Limited data analytics information was being manually extracted by the client on behalf of customers. These analytics did not drive intelligent business decisions.
4. Analytics was diagnostic and descriptive, but not predictive and prescriptive.

Using CRMA and Einstein, we built an innovative Analytics App that provides the following:

• Cutting-edge data analytics with integrated AI insights and predictions
• Intelligent recommendations from Einstein Discovery AI

The result enabled the client to drive channel efficiency and marketing optimization. The benefits included increased ROI on marketing investment and increased patient loyalty and enhanced patient experience. Let's check out some of the screenshots from this solution.

The following is the home page view:

Figure 1.13 – Home page of the Attribution Analytics solution

The following is the ROI insights page view:

Figure 1.14 – ROI analytics in the Attribution Analytics solution

The following is the lifetime customer value (LCV) page view:

Figure 1.15 – LCV analytics in the Attribution Analytics solution

Other use cases for CRMA include insights into the following:

• Sales performance, such as actuals and forecast versus target.
• Lead conversion by lead source, region, and team member.
• ROI on marketing campaigns.
• Case management and service team performance.
• Global sales breakdown by region, territory, and country.
• Forecast sales by region and team.
• Customer attrition.
• Customer acquisition.
• Customer satisfaction.
• Supply chain performance, identifying bottlenecks, and opportunities.
• Financial services compliance with forward-looking analytics to identify clients at risk of breach.
• Higher education performance and student success.
• Opportunities for educational institutions to cross-sell and up-sell.
• White space analysis for sales organizations.
• Fundraising performance for not-for-profit (NFP) institutions.
• Donor segmentation for NFP appeals.
• Project management risks and issues.
• Resource assignment and optimization in professional services businesses.
• Geospatial data.
• Revenue, expenses, and profit, and many more!

Hopefully, this section has helped you understand how we can use CRMA to solve problems and grasp opportunities. Remember, it is all about Data -> Insight -> Action, as shown in Figure 1.1.

In the next section, we are going to examine your organization and evaluate how you are making use of data and analytics.