Is AI the Next CRM Battleground?

by Drew Robb



“For the first time ever, AI is moving into the mainstream thanks to the convergence of increasing compute power, the massive amounts of data and the advances in machine learning,” said Allison Witherspoon, director, product marketing, Salesforce Einstein. “Everyone is already being impacted by AI daily, without even realizing it.”

She offered examples such as Apple’s Siri leveraging natural language processing to recognize voice commands, Facebook’s deep learning facial recognition algorithm that identifies a person with nearly 98 percent accuracy and sites such as Spotify that use machine learning to understand how each catalog item relates to one another and customer preferences. That said, AI is still at the very early stages when it comes to being part of business processes such as CRM.

CRM meets AI

Salesforce got the AI ball rolling when it purchased RelateIQ a couple of years back. It then released SalesforceIQ that automatically captures, analyzes and surfaces information from email, calendars and more, particularly for business development and sales. It creates a unified address book for a sales team, tracks activity by process or pipeline, and generates real-time relationship insight tailored to workflows.

“There is huge demand for AI-powered systems that deliver greater intelligence as companies look to transform for the digital age,” said Witherspoon.

Things were just stepped up a notch with the release of Salesforce Einstein, which has embedded AI capabilities, such as machine learning, deep learning and natural language processing. There are already 17 generally available Einstein features across five of the Salesforce Clouds, with new Einstein features being developed for every Salesforce Cloud. Enterprises can look for these to come out either in the Winter ‘17 release, or the following next year. The company has hundreds of developers and data scientists assigned to this area, which it regards as being of high strategic importance.

Witherspoon said machine learning algorithms can analyze billions of transactions and variables to determine which customers are most likely to purchase a particular product. This opens the door to greater levels of automation, personalization of experiences and improved decision making for sales, customer support and marketing.

Sales reps, for example, can use Predictive Lead Scoring to focus on closing the best leads and Opportunity Insights to understand when a deal is trending up or down so they can follow up appropriately. With Automated Activity Capture, email and calendar activity is automatically logged with the right Salesforce record, and analyzed to deliver predictions. In addition to those AI features, Salesforce also has Recommended Case Classification for call center teams to enable predictive routing of cases to the right agent, Recommended Responses so agents get the best responses to questions, and Predictive Close Times to predict the time needed to resolve an issue. Alternatively, marketers can use Predictive Scoring to understand every customer’s likelihood to engage with an email, Predictive Audiences to build custom audience segments, and Automated Send-time Optimization to predict the optimal time to deliver messages.

The Competition Responds

Gartner notes that Salesforce leads the CRM marketplace with a 20 percent stake, followed by SAP at 10 percent, Oracle at 8 percent and Microsoft at 4 percent. Salesforce hopes to extend its lead via Einstein. But don’t expect it to have things its own way easily.

IBM just unveiled new Watson-themed tools for marketing, CRM and customer analytics. Details of past purchases, customer behavior, delivery preferences and more are brought to the fore in IBM’s new cognitive solutions for marketers. The goal is to provide CRM with the ability to identify correct audiences to create a winning combination of campaign elements.

Other tools include IBM Watson Customer Experience Analytics, which taps into shopping patterns and buying trends to create models for customer segmentation. The IBM Watson Content Hub mines the data buried in a company’s content management system and automatically tags content including images, videos and documents. This is helpful as it avoids the prospect of marketers floundering for the right data inside a vast content repository.

“IBM is bringing Watson cognitive capabilities to millions of professionals around the world, putting a trusted advisor and personal analyst at their fingertips,” said Harriet Green, general manager Watson IoT, cognitive engagement and education.

Microsoft, too, is getting in on the act. A new release of Microsoft Dynamics 365 is being stuffed full of cloud-based AI features that integrate with ERP, CRM, supply chain and other applications. The company's recent acquisition of LinkedIn plays a big part in this. Analytics engines can harness CRM, ERP, LinkedIn and Microsoft document data to offer up sales leads and marketing insight.

Intelligent Partner Apps

The vast Microsoft partner network also comes into play. Versium, for example, has integrated its automated predictive targeting application into Microsoft Dynamics 365. This allows Dynamics users to leverage AI to prioritize leads and create high-value audiences for improved marketing campaign performance.

“The new wave of AI use cases is applying predictive modeling to the existing customer base to extract more value, aka cross-sell and upsell, and modeling to help identify the most loyal customers,” said Chris Matty, CEO and co-founder, Versium. “This is important because it’s widely accepted that a significant percent of a company’s revenue and value often comes from the top 20 percent of customers. The same principles of predictive modeling are being applied to retention to predict who is most likely to cancel or not renew a service.”

Take the concept of adopting new technology. Some people are very conservative, and some are risk takers. But how do you tell? The hope is that AI will be able to model the individual characteristics of decision-makers within a company based on their consumer attributes. For example, if a decision-maker is into rock climbing, that tends to indicate risk-taker attributes and feeds into their likelihood to take a chance on new technology.

For contact centers, AI changes service from a B2C interaction to a B2M2C (business to machine to consumer) interaction. In other words, you can now expect many more interactions to filter through an ecosystem of smart and connected devices that may actually participate in the resolution.

For example, machine learning can adaptively learn how to solve similar problems and patterns over time. You also see this with bots that are infiltrating messaging apps like Slack, and soon we will see this take a place in contact centers as well. That said, humans continue to provide a crucial component.

Advisory versus Autonomy











Scratching the Surface




  This article was originally published on Thursday Dec 8th 2016
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