Stephanie Buscemi, EVP Product and Solutions Marketing, Salesforce
Stephanie Buscemi, EVP Product and Solutions Marketing, Salesforce introduces Salesforce Einstein Analytics in Chicago PHOTO: Linda Dunlap @lldunlap

If Salesforce executive vice president Stephanie Buscemi did her job right today, her audience at the Chicago stop of the Salesforce World Tour is now convinced the powers of a data scientist are at their fingertips.

Assuming they adopt Salesforce Einstein Analytics, that is.

Buscemi and her team unveiled Salesforce's latest Einstein-infused cloud solution this morning. It promises to help sales, marketing and service professionals make better decisions, up to 38 percent faster by leveraging Artificial Intelligence (AI). It proposes to achieve this via contextually relevant, self-service analytics apps that can tell you what is happening, why it is happening, what is likely to happen and what action you should take.

“Salesforce Einstein Analytics is not a reflection tool, but a guidance system,” Ketan Karkhanis, SVP and general manager of Salesforce Analytics, told CMSWire in a pre-briefing prior to the announcement.

Kharkanis shared the example of a salesperson unsure if he will make his quarterly numbers. He could leverage Einstein Analytics for help. It will look at customer, competitor and pipeline data and make recommendations like, "Set up a meeting with customer X, when you do, sales close five days faster," or "the customer you thought was loyal is talking to your competitor pretty often, maybe you should take him to lunch" or "you need buy-in from three managers to close the deal and one of them isn't even paying attention, here's what you might want to do about it," and so on.

Pre-Built Apps, Einstein Discovery, Trailhead

Salesforce Einstein Analytics takes its place in the Salesforce and Salesforce customer ecosystem in three different ways:

Salesforce Einstein Analytics Apps

These are preconfigured with role-specific key performance indicators (KPIs). The data-driven Sales Analytics app, for example, makes it easy for managers and sales reps to collaborate, manage forecasts, pipelines and performance from anywhere, at any time. 

The Service Analytics app provides call center managers with the insights they need to win customer satisfaction, agent efficiency and channel optimization. The B2B Marketing Analytics App brings data-driven marketers insights on pipeline, engagement and campaign performance.  

Salesforce Einstein Analytics also provides Salesforce customers with tools to build and customize their own custom analytics apps on the Salesforce platform.

Einstein Discovery

Insights and stories are buried in your data, but even an elite team of data scientists working around the clock can't surface them all. This is AI's sweet spot. It uses machine learning to detect statistical trends, reveal patterns and present them to end users in ways they can understand, including auto-generated slide presentations that contain visualizations and talking points. 

It also suggests courses of action. Moreover Einstein Discovery is an open solution, meaning users can see what data is being used, what algorithms are being applied and more. Salesforce is offering a free trial of Einstein Discovery available for anyone who has data they want to explore.

Seeding the Salesforce Ecosystem with Analytics Trailblazers

Salesforce has geared-up to develop “analytics trailblazers” via 12 online learning modules on the interactive, guided and gamified Trailhead platform.

Data Science Insights, No Ph.D. Required ... Really?

Salesforce's claim that everyone can be a data scientist or even a "citizen data scientist" is bold to say the least, but Karkhanis insisted that 250,000 independent Arbonne beauty and skincare sales reps are doing exactly that with Einstein Analytics. 

CMSWire spoke to Constellation Research analyst Holger Mueller to get his take on whether ordinary business professionals could actually get the same kind of insights from software as data scientists who have studied years to create algorithms, run intelligent SQL queries and code in R.

"It is the software that needs to get the insights — not humans — no matter if they are data scientists or end users,” said Mueller, adding that, “Data scientists are quickly becoming obsolete as self-learning neural networks can determine business outcomes better and more consistently than the average professional on an average day. The very good human on a very good day may never be caught .... But the rest, yes. Software never sleeps, always works and doesn't have bad days," he said.